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Scheduling algorithm for delay and jitter reduction of periodic tasks in real-time systems

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Real-time systems, especially software control systems, are developed to meet the requirements of real-time automation systems. One such crucial requirement is reducing the delay and jitter of periodic tasks in such systems. In this paper, we present a new method for reducing delays and jitters of periodic tasks, which are enforced by the operating system, control tasks, kernel mechanisms, etc. Our algorithm is evaluated and compared with other scheduling algorithms in terms of jitter. The effectiveness of our algorithm is confirmed by the experimental results. Streszczenie. W pracy przedstawiono algorytm szeregowania dla redukcji fluktuacji i opóźnień zadań okresowych w systemach czasu rzeczywistego. Zastosowanie takiego algorytmu gwarantuje przewidywalność oraz poprawia efektywność działania systemów. Dodatkowo dzięki niemu jest możliwe zmniejszenie fluktuacji czasu odpowiedzi zadań okresowych. Możliwości działania prezentowanego algorytmu zostały potwierdzone w badaniach symulacyjnych. (Algorytm szeregowania dla redukcji fluktuacji i opóźnień zadań okresowych w systemach czasu rzeczywistego) Keywords: scheduling algorithms, real-time systems, periodic tasks, reducing the delay and jitter of periodic tasks. Słowa kluczowe: algorytmy szeregowania, systemy czasu rzeczywistego, redukcja fluktuacji i opóźnień zadań okresowych. Introduction In real-time control applications many periodic tasks are executed which are associated with periodic activities such as periodic execution of control tasks, action planning, etc. These tasks are periodically invoked by the scheduler implemented in the kernel of the operating system. The factors which degraduate the performance of realtime systems are delays and jitter appearing during the execution of periodic tasks. A jitter can be interpreted as disturbances acting upon the system which as a result of interrupts, calls of the operating system kernel, the activities of the scheduler, control program[...]


  In this paper, we study a new power allocation scheme for OFDM-based green cognitive radio networks (CRNs). Due to the secondary transmission, the primary users (PUs) and the secondary users (SUs) cause mutual interference. Instead of using the conventional interference power constraint (IPC) to protect the PUs in the primary system, we propose a criterion in the form of an upper bound on the rate loss of PU due the SU transmission. Under the suggested constraints along with the SUs transmission constraint, the effective power allocation scheme in green CRN for the SU to maximise its transmission rate is given. 1. INTRODUCTION A cognitive radio (CR) is a new concept for the wireless communication system. The idea of CR was first presented by J. Mitola III and G.Q. Maquire [1]. Later S. Haykin [2] treated CR as brain-empowered wireless communications. The main goal of CR is to improve the utilisation of a natural resource: the radio electromagnetic spectrum. CR is considered as a future radio technology that should be aware of its surrounding environment and internal state. Moreover, the CR system may be deployed and distributed in network, ad hoc, and mesh architectures. Thus, it serves both - licensed (primary users, PUs) and unlicensed users (secondary users, SUs). On the other hand, the CR network should be capable of using bands assigned to unlicensed users and utilising the licensed part of a radio spectrum without harmful interference. In CR, the secondary users need to sense the idle channel. Once an idle channel is sensed, the secondary system will access this channel. Recently, SUs are allowed to transmit their data with the PU over the same spectrum band simultaneously on condition that the resultant interference at the PU receiver is below tolerable interference threshold. This kind of architecture is referred to as spectrum underlay [3]. Otherwise, in this approach simultaneous cognitive and non cognitive [...]


  Elliptic curve cryptography (ECC) is a complex computational method and before its implementation some parameters have to be selected. Several software implementations of ECC systems have been realised. Nevertheless, the most popular in the wireless ad hoc and sensor networks are demands in the hardware implementations. Most of them are implemented on Application Specific Integrated Circuits (ASIC) and only two were implemented on Field Programmable Gate Array (FPGA). This paper surveys the ECC systems and investigates different implementations on wireless ad hoc and sensor networks. 1. INTRODUCTION Ad hoc networks have received their name due to the fact that there is no predefined structure or infrastructure of communication, but they consist of nodes that relay information to their neighbours possibly on behalf of other neighbours. Typical ad hoc networks are composed of heterogeneous mobile devices, such as cellular phones, PDAs, laptops, etc. and fixed equipment (base stations, access points, etc.). Wireless sensor networks (WSNs) are a particular type of ad hoc networks, in which the nodes are equipped with sensing facilities (thermometer, sensing device, etc.). A necessary component of wireless ad hoc and sensor networks is the ability to reliably authenticate the communication between the nodes and other network entities. The provision of security services such as authentication, confidentiality, integrity is critical in order to deploy the wireless ad hoc and wireless sensor networks in commercial or military environments. Some traditional security mechanisms, such as authentication protocols, digital signature and encryption are used in the ad hoc networks [1]. However, these mechanisms are not sufficient by themselves. Security requirements are mainly satisfied in WSNs through a symmetric key cryptography [2, 3]. These protocols are based on the keys concept. However, due to the limitation on memory resources[...]


  An orthogonal frequency-division multipleaccess- based cognitive radio (CR) network is considered in this study, where primary users (PUs) dynamically sense the spectrum and opportunistically use the available channels. The objective is to maximise the CR network throughput under the PUs maximum interference constraint. The optimal resource allocation problem for this network with QoS requirements is presented and an algorithm is proposed. Simulations confirm that the proposed algorithm is nearly the optimal solution. Streszczenie: W artykule przedstawiono kognitywne sieci radiowe z modulacją OFDMA, w których licencjonowani użytkownicy (PUs) mogą badać widmo elektromagnetyczne i wykorzystywać dostępne kanały transmisji. W artykule sformułowano problem maksymalizacji przepustowości kognitywnej sieci radiowej, uwzględniając ograniczenia powodowane zakłóceniami i wymaganiami transmisji multimedialnej. Jako rozwiązanie problemu przedstawiono algorytm alokacji zasobów. Przeprowadzona symulacja potwierdziła, że zaproponowany algorytm jest prawie optymalnym rozwiązaniem tego zagadnienia. Keywords: cognitive radio networks, quality of service, resource allocation, optimization Słowa kluczowe: kognitywne sieci radiowe, jakość usługi, alokacja zasobów, optymalizacja 1. INTRODUCTION Most current wireless applications widely exploit the spectrum, but investigations show that many licensed frequency bands are far underutilised [1]. Cognitive radio (CR), a technique first introduced by J. Mitola [2], is a promising technology to solve the problem with underutilised spectrum. The CR nodes allow to share the spectrum with licensed primary user (PU) nodes and adapt their transmission characteristics according to the instantaneous behaviour of licensed users. The CR networks, based on the CR devices, can improve the overall spectral utilisation. However, the maximisation of the throughput of the network leads to the undesirable interfer[...]


  This paper presents a joint adaptive modulation and coding (AMC) and power allocation problem in heterogeneous cellular networks from scalable video coding (SVC) video multicast transmission. Then, the problem for joint power control and modulation and coding scheme (MCS) for each sub-channel for SVC video multicast is detailed. As a solution of this problem, an algorithm is proposed to maximise the defined utility of the system. The simulation study shows that its performance is comparable to other solutions. Streszczenie: W artykule przedstawiono zagadnienie alokacji mocy oraz przydzielania stosownego schematu modulacji i kodowania (AMC) w heterogenicznych sieciach komórkowych z transmisją multikastową. Jako rozwiązanie problemu zaproponowano algorytm maksymalizujący zdefiniowaną użyteczność systemu. Badania symulacyjne pokazują, że uzyskane wydajność jest porównywalna z innymi metodami. Keywords: scalable video multicast, power allocation problem, adaptive modulation and coding, heterogeneous cellular networks. Słowa kluczowe: skalowalna transmisja multikastowa wideo, zagadnienie alokacji mocy, adaptacyjna modulacja i kodowanie, heterogeniczne sieci komórkowe 1. INTRODUCTION Most modern mobile devices have the capabilities to receive different frequency bands. For instance, smartphones can be connected to 5 G or 4 G LTE, laptops that use the Wi-MAX networks, etc. Moreover, most cities are covered by 4 G or 3 G wireless technology, where additionally numerous Wi-Fi hot spots are operating. All this makes that we are dealing with an infrastructure, which is defined as heterogeneous cellular networks [1], [2]. Very often, the different types of networks are owned by the same operator. This means that there may be cooperation between base stations belonging to different networks, which in total can greatly improve the efficiency of the whole network. The multicast technique is an efficient mechanism for video transmiss[...]


  Cloud computing provides applications, hardware platforms, and infrastructure to a large number of private and corporate users. Most of them expect an efficient service from cloud providers, including low cost of ownership. Moreover, the implementation of a number of computing clouds is linked to a number of objectives, such as quality of service, cost-effectiveness, or energy savings. Including these goals, often contradictory, is a serious challenge for cloud computing designers. It is recalled that cloud computing can be defined as ‘a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned, and presented as one or more unified computing resources based on servicelevel agreements established through negotiation between the service provider and consumers’ [1]. An available example of a cloud computing infrastructure platforms is Microsoft Azure [2], Google App Engine [3], Aneka [4], etc. The quality of the cloud computing determines the performance of the entire system. If the provider does Fig. 1. Layered cloud computing architecture. not provide service according to Service Level Agreements (SLAs) [5], then the clients will depart from it to find such a service elsewhere. So the key is an efficient resource allocation system. Fig. 1 shows the architecture of a typical cloud computing environment. Its first part is the customers who can rent Virtual Machines (VMs) from cloud providers. During the rental period, customers specify their desire. The key element of the cloud is the scheduler, whose purpose is to schedule the VM requests and connection requests in the fastest possible way. At the same time, requirements such as energy savings or QoS constraints must be met. An attempt to summarize the challenges awaiting designers of cloud computing is presented in the article [6]. The crucial problem of the cloud computing [...]


  According to the research study given by ATT [1], 70 percent of data services will take place indoors in the next years. Therefore, it is becoming increasingly important to conduct research into the development of the femtocell network, to increase their capabilities, especially for the transmission of multimedia data, which is characterized by the requirements described by the QoS parameters. Moreover, the provision of high transmission capacity to each femtocell requires good, proven techniques, as evidenced by the research papers [2], [3]. Femtocells are cost-effective, small-coverage networking systems, where a femtocell access point (FAP) or femtocell base station is localised at home or in an office. They are connected to a service provider's internet network. The entire overlaid network is referred as a two-tier femtocell network [2]. To fully exploit the benefits of the two-tier architecture, the problems of resource allocation (power control, rate allocation, etc.) are a big challenge for achieving a significant performance level and minimal electricity consumption. Cognitive femtocells are a promising new technique that allows for even better use of radio resources [5]. They have been studied, among others, by [6], [7], [8]. In the first paper, presented by N. K. Gupta et al. [6] power control and subcarrier allocation for an OFDMA based underlay cognitive radio network was introduced. An admission control and channel allocation algorithm based on particle swarm optimization for cognitive cellular networks has been described by A. Martínez-Vargas et al. [7]. In the paper by S. Saadat et al. [8] a QoS guaranteed resource allocation scheme for cognitive femtocells in LTE networks has been proposed. The interference mitigation in the cognitive radio-based femtocells has been carried out by Kpojime et al. [9]. The main goal of this paper is to introduce a new optimal power allocation scheme in the femtocell network.[...]


  1. INTRODUCTION A cognitive radio (CR) [1] is a wireless device that senses the surrounding radio environment and opportunistically accesses the unutilised spectrum bands based on its assessment of the activities of the surrounding primary (licensed) network. Otherwise, CR is a system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximise throughput, mitigate interference, etc. Cognitive radio networks (CRNs) are foreseen as the future of wireless communication. In CRN a secondary (unlicensed) user (SU) in the secondary network is allowed to access the spectrum that is originally allocated to the primary users (PUs) when the spectrum is not used by any PU. This secondary spectrum usage method is called opportunistic spectrum access [2]. In this way, the spectrum utilisation efficiency can be greatly improved. Moreover, this is a very crucial feature of CR networks which allow users to operate in licensed bands without a license. CR devices and femtocell system is a very promising technology for possible use in two-tier cognitive femtocell networks [3]. Then, a dynamic and opportunistic access to free frequencies is possible using the socalled holes. Two-tiered cognitive radio networks provide a high cell capacity at a low cost of construction [4]. Examples of such networks can be found among the various home hotspots and urban hot spots [5]. The femtocell radio range ranges from 10 to 50 m, while the radius of the macrocell reaches a value of from 300 to 2000 m [6]. Recently, it has been reported in the literature that harvesting energy can be achieved from other ambient sources such as wind, solar, vibration, etc. As presented in the paper by Vullers et al. [7] radio-frequency (RF) signals can power a network of low-power devices. Furthermore, the amount of energy harvested from the primary channel [...]

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